{"id":"https://openalex.org/W4402351496","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650798","title":"Exploiting Class Feature Alignment for Personalized Federated Learning in Mixed Skew Scenarios","display_name":"Exploiting Class Feature Alignment for Personalized Federated Learning in Mixed Skew Scenarios","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351496","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650798"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004324773","display_name":"Wentao Yin","orcid":"https://orcid.org/0009-0002-1880-1044"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wentao Yin","raw_affiliation_strings":["Yunnan University,Kun Ming,China"],"affiliations":[{"raw_affiliation_string":"Yunnan University,Kun Ming,China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100321495","display_name":"Yuhang Zhang","orcid":"https://orcid.org/0000-0001-6404-9952"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhang Zhang","raw_affiliation_strings":["Central South University,Chang Sha,China"],"affiliations":[{"raw_affiliation_string":"Central South University,Chang Sha,China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004324773"],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":0.3862,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66772628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8244264125823975},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.7947252988815308},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6612727046012878},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5943940281867981},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.36561793088912964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3326757550239563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8244264125823975},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.7947252988815308},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6612727046012878},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5943940281867981},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.36561793088912964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3326757550239563},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.41999998688697815,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W2160815625","https://openalex.org/W2163922914","https://openalex.org/W2744999500","https://openalex.org/W2807006176","https://openalex.org/W2919115771","https://openalex.org/W2963819344","https://openalex.org/W2990789643","https://openalex.org/W2995022099","https://openalex.org/W2996974038","https://openalex.org/W3005776401","https://openalex.org/W3006555759","https://openalex.org/W3012968339","https://openalex.org/W3035453001","https://openalex.org/W3047304572","https://openalex.org/W3080934299","https://openalex.org/W3089578458","https://openalex.org/W3099314130","https://openalex.org/W3112044954","https://openalex.org/W3118608800","https://openalex.org/W3126083338","https://openalex.org/W3128515475","https://openalex.org/W3129097267","https://openalex.org/W3129603732","https://openalex.org/W3133814152","https://openalex.org/W3169361100","https://openalex.org/W3193589553","https://openalex.org/W3213291156","https://openalex.org/W3213815372","https://openalex.org/W4221163233","https://openalex.org/W4285302300","https://openalex.org/W4285326356","https://openalex.org/W4285762978","https://openalex.org/W4285876308","https://openalex.org/W4287332481","https://openalex.org/W4287906413","https://openalex.org/W4299518610","https://openalex.org/W4300427714","https://openalex.org/W4303647819","https://openalex.org/W4312429505","https://openalex.org/W4319990553","https://openalex.org/W6682132143","https://openalex.org/W6728757088","https://openalex.org/W6738383168","https://openalex.org/W6752029299","https://openalex.org/W6759238902","https://openalex.org/W6773813173","https://openalex.org/W6774978782","https://openalex.org/W6779174293","https://openalex.org/W6779269186","https://openalex.org/W6780224944","https://openalex.org/W6784336702","https://openalex.org/W6787972765","https://openalex.org/W6789305514","https://openalex.org/W6790808513","https://openalex.org/W6791102956","https://openalex.org/W6791444617","https://openalex.org/W6793191782","https://openalex.org/W6795281715","https://openalex.org/W6795843344","https://openalex.org/W6796484261","https://openalex.org/W6838907135","https://openalex.org/W6845905529","https://openalex.org/W6850004136","https://openalex.org/W6854708048"],"related_works":["https://openalex.org/W4290802965","https://openalex.org/W97789383","https://openalex.org/W4289406402","https://openalex.org/W2727156679","https://openalex.org/W3087516072","https://openalex.org/W2067997904","https://openalex.org/W2364071303","https://openalex.org/W1483053255","https://openalex.org/W2896097814","https://openalex.org/W20221657"],"abstract_inverted_index":{"Personalized":[0],"federated":[1,18,65],"learning":[2,19,66],"aims":[3],"to":[4,12,47,82],"provide":[5,48,106],"individualized":[6],"models":[7,50],"for":[8,51,87,95,111],"each":[9,88,112],"client,":[10],"adapting":[11],"local":[13,126,131],"data.":[14],"However,":[15],"existing":[16],"personalized":[17,49,64,108],"solutions":[20],"primarily":[21],"address":[22],"single-data":[23],"heterogeneity":[24],"scenarios,":[25],"such":[26],"as":[27],"label":[28,59,102,140],"skew":[29,37],"or":[30],"feature":[31,57,80,85,93,137],"skew.":[32,103],"When":[33],"both":[34,56],"types":[35],"of":[36,43,79,90,101,157],"exist,":[38],"there":[39],"is":[40],"a":[41,62,77,115,119,135],"lack":[42],"work":[44],"discussing":[45],"how":[46],"clients.":[52],"This":[53],"paper":[54],"considers":[55],"and":[58,139],"skew,":[60],"proposing":[61],"novel":[63],"framework":[67],"called":[68],"Federated":[69],"Learning":[70],"with":[71,124],"Class":[72],"Feature":[73],"Adjuster(FedCFA).":[74],"FedCFA":[75,159],"employs":[76],"set":[78],"adjusters":[81],"separately":[83],"seek":[84],"centers":[86],"category":[89],"data,":[91],"accomplishing":[92],"alignment":[94],"clients":[96],"while":[97],"avoiding":[98],"the":[99,125,130,144,154,158],"impact":[100],"Additionally,":[104],"we":[105],"adaptive":[107],"classifier":[109,122],"heads":[110],"client":[113],"through":[114],"similarity-based":[116],"method,":[117],"ensuring":[118],"highly":[120],"relevant":[121],"head":[123],"data":[127],"distribution.":[128],"Finally,":[129],"model":[132],"updates":[133],"in":[134,148],"consistent":[136],"space":[138],"space,":[141],"thus":[142],"mitigating":[143],"performance":[145,156],"degradation":[146],"issue":[147],"label-skewed":[149],"scenarios.":[150],"Extensive":[151],"experiments":[152],"demonstrate":[153],"superior":[155],"method":[160],"across":[161],"various":[162],"skewed":[163],"scenarios":[164],"on":[165],"different":[166],"datasets.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
